path_files = glob(os.path.join(path_data, "*.edf"))
len(path_files)36Checking annotations
We’re going to check all the different annotations we have.
We are going to load all the files and check their .annotations attribute to extract the important ones:
raws = [mne.io.read_raw_edf(path, preload=False, verbose=False) for path in path_files]anns = np.concatenate([raw.annotations.description for raw in raws])
len(anns)70822It’s easier to understand the information if we represent it visually:
cntr = Counter(anns)
plt.figure(figsize=(15,6))
plt.bar(cntr.keys(), cntr.values())
plt.xticks(rotation=90)
plt.xlabel("Annotation")
plt.ylabel("Counts")
plt.title("Count of each annotation")
plt.show()
It can be usefull as well to store this information as a .csv:
df_anns = pd.DataFrame.from_dict(cntr, orient='index', columns=['Counts'])
df_anns.index.set_names('Annotation', inplace=True)
df_anns.head()| Counts | |
|---|---|
| Annotation | |
| Montage:PR, Ref | 56 |
| Start Recording | 37 |
| Recording Analyzer - Sleep Events | 36 |
| Recording Analyzer - Auto-Staging | 36 |
| Recording Analyzer - ECG | 36 |
df_anns.to_csv("annotations.csv")